Project Summary Autism spectrum disorder (ASD) is a heterogeneous disorder characterized by repetitive and stereotyped be- havior and dif?culties in communication and social interaction. It is now one of the most prevalent psychiatric disorders in childhood, but it is also a lifelong condition, adversely affecting an individual's social relationships, independence and employment well into adult. A major barrier to creating effective treatments for autism is the lack of understanding of the speci?c brain mechanisms involved and how these are related to speci?c behavioral symptoms. We propose to develop novel statistical methods for combining heterogeneous imaging and behavioral data to understand how properties of complex brain networks give rise to behavioral phenotypes in autism and other neuropsychiatric disorders. The ?rst contribution of this project is to develop novel image analysis methods to extract individualized features of complex brain networks from imaging data. This includes powerful method for describing the shape of gray matter in brain networks based on diffeomorphic image registration and a rigorous method for inferring an individual's functional connectivity based on a hierarchical Bayesian model. The next contribution is a novel method to capture the topology of brain networks simultaneously across all scale levels of connection strength. Finally, we will develop Bayesian statistical methods for ?nding correlations in high- dimensional and heterogeneous data, and we will use this to analyze the relationship between brain networks and behavior. This project includes a strong collaborative and multi-disciplinary team with expertise in computer science, statistical data analysis, neuroimaging, and clinical autism care. A primary goal of this project is to create open-source software that is used by the neuroscience community to advance research in understanding the brain basis of behavior in neuropsychiatric disorders.